B .T E C H . O P T I M I S A T I O N O F P R O C E S S P A R A M E T E R S O N C N C L A T H E M A C H I N E M A Y 2 0 1 8
OPTIMISATION OF PROCESS PARAMETERS ON CNC LATHE MACHINE A Project Report Submitted In Partial Fulfillment of the Requirements For the award of the Degree of
BACHELOR OF TECHNOLOGY in
MECHANICAL ENGINEERING by
DIVYANSHU SRIVASTAVA KUNAL SINGH ROHIT PANDEY CHETAN ANAND ASHISH KUMAR GUPTA
(1413240067) (1413240091) (1413240173) (1413240053) (1413240042)
Under the supervision of
MR. ANUJ DIXIT
DEPARTMENT OF MECHANICAL ENGINEERING
GREATER NOIDA INSTITUTE OF TECHNOLOGY Plot no. 7, Knowledge Park Park – II, Greater Noida, U.P(201310) Affiliated to Dr. A.P.J. Abdul Kalam Technical University, Lucknow, (Formerly Uttar Pradesh Technical University, Lucknow, U.P.) MAY, 2018
OPTIMISATION OF PROCESS PARAMETERS ON CNC LATHE MACHINE A Project Report Submitted In Partial Fulfillment of the Requirements For the award of the Degree of
BACHELOR OF TECHNOLOGY in
MECHANICAL ENGINEERING by
DIVYANSHU SRIVASTAVA (1413240067) KUNAL SINGH (1413240091) ROHIT PANDEY (1413240173) CHETAN ANAND (1413240053) ASHISH KUMAR GUPTA (1413240042) (1413240 042)
Under the supervision of
MR. ANUJ DIXIT
DEPARTMENT OF MECHANICAL ENGINEERING
GREATER NOIDA INSTITUTE OF TECHNOLOGY, GREATER NOIDA Affiliated to Dr. A.P.J. Abdul Kalam Technical University, Lucknow (Formerly Uttar Pradesh Technical University, Lucknow, U.P.) MAY, 2018
GREATER NOIDA INSTITUTE OF TECHNOLOGY PLOT NO. 7, K.P. II, GREATER NOIDA, UP-201310 Affiliated to Dr. A.P.J. Abdul Kalam Technical University, Lucknow, UP (Formerly known as Uttar Pradesh Technical University, Lucknow)
CERTIFICATE This is to certify that project report entitled “OPTIMISATION OF PROCESS PARAMETERS ON CNC LATHE MACHINE ” which is submitted by Divyanshu Srivastava , Kunal Singh , Chetan Anand , Rohit Pandey and Ashish Kumar Gupta in
partial fulfillment of the requirements for the award of degree Bachelor of Technology in Department of Mechanical Engineering from Greater Noida Institute of Technology, affiliated to Dr. A.P.J. Abdul Kalam Technical University, Lucknow is a record of the candidates own work carried out by them under my supervision. The matter embodied in this project report is original and has not been submitted for the award of a ny other degree.
(Mr. ANUJ DIXIT) Supervisor & Project Co-ordinator, ME Deptt.
( Dr. SUDHIR KUMAR) Prof. & HOD ME Deptt.
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DECLARATION We hereby declare that this submission is our own work and that, to the best of our knowledge and belief. It contains no material previously published or written by another person nor material which to a substantial extent has been accepted for the award of o f any other degree or diploma of the university u niversity or other institute of higher learning, except where due acknowledgment has been made in the text.
Signature: Name: Divyanshu Srivastava Roll no: 1413240067 Date: /05/2018
Signature: Name: Kunal Singh Roll no: 1413240091 Date: /05/2018
Signature: Name: Rohit Pandey Roll no: 1413240173 Date: /05/2018
Signature: Name: Chetan Anand Roll no: 1413240053 Date: /05/2018
Signature: Name: Ashish Kumar Gupta Roll no: 1413240042 Date: /05/2018
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ACKNOWLEDGEMENT ACKNOWLEDGEME NT It gives us a great sense of pleasure to present the report of B.Tech project undertaken during B.Tech. Final Year. We express our sincere gratitude to our respected supervisor, Mr. Anuj Dixit, (Assistant Professor, Department of Mechanical Engineering, Greater Noida Institute of Technology, Greater Noida) for his invaluable inspiring guidance and constant encouragement during the period of project work. His sincerity, thoroughness and perseverance have been a constant source of inspiration for us. It is only his cognizant efforts efforts that our endeavors have seen light of the day. We also take the opportunity to acknowledge the contribution of Professor Sudhir Kumar, (Head, Department of Mechanical Engineering, Greater Noida Institute of Technology, Greater Noida) for his full support and assistance during the development of the project. We also like to acknowledge the contribution of all faculty members of the department for their kind assistance during the development of our project. Last but not the least, we acknowledge our friends for their contribution in the completion of this project.
Signature: Name: Divyanshu Srivastava Roll no: 1413240067 Date: /05/2018
Signature: Name: Kunal Singh Roll no: 1413240091 Date: /05/2018
Signature: Name: Rohit Pandey Roll no: 1413240173 Date: /05/2018
Signature: Name: Chetan Anand Roll no: 1413240053 Date: /05/2018
Signature: Name: Ashish Kumar Gupta Roll no: 1413240042 Date: /05/2018
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ABSTRACT Advanced makers, looking to stay focused in the business sector, depend on their assembling architects and creation work force to rapidly and adequately set up assembling procedures for new items. Taguchi Parameter Design is an intense and effective technique for enhancing quality and execution yield of assembling procedures, along these lines a capable apparatus for meeting this test. concerned with optimizing the process or say input parameters of a CNC machine which in turn helps us to get a better and optimised output. Optimised in terms of technology, economics and knowledge. Into this project we have went through a number of research work done by various researchers over a variety of materials on CNC machine through various machining operations. In our project our material under operation turning is an Aluminium 6063 T6 alloy which we have selected due to its various applications in the various different industries and also due to cost effectiveness and availability. The machine over which we have performed our turning operation is 3 axis CUB/XXZ semi-automatic CNC turret lathe having 8 tool inserting positions. We have also worked upon Minitab 2015 software for the statistical analysis of our project by calculating the means, clamor proportions and a nd signalto-noise ratios for each turning operation we have performed. Also a L9 orthogonal array was used to decide the combination of values for each turning operation on Aluminium 6063 T6. The process parameters we have considered under our project for an optimised output are feed rate (in mm per spindle revolution), depth of cut (in mm) and spindle speed (in rpm). With the help of an L9 orthogonal array we have decided a set of combination of values for our considered process parameters for an optimised surface roughness and material removal rate.
v
CONTENTS CERTIFICATE
ii
DECLARATION
iii
ACKNOWLEDGEMENT
iv
ABSTRACT
v
CONTENTS
vi
LIST OF FIGURES
viii
LIST OF TABLES
ix
CHAPTER: 1 INTRODUCTION
1-7
1.1 Background of CNC
1-2
1.2 Introduction of CNC
2-3
1.3 Basic Principle of CNC
3
1.4 Classification of CNC
3-4
1.4.1 Point-to-point system
3-4
1.4.2 Contouring system
4
1.5 Parameters of CNC
5-6
1.5.1 Cutting parameters for CNC turning
5-6
1.5.1.1 Cutting speed
5
1.5.1.2 Cutting feed
5
1.5.1.3 Spindle speed
5
1.5.1.4 Feed rate
5
1.5.1.5 Axial Depth of Cut
6
1.5.1.6 Radial Depth of Cut
6
1.6 Advantages of CNC
6
1.7 Limitations of CNC
6-7
1.8 Applications of CNC CHAPTER: 2 LITERATURE REVIEW
8-14
2.1 Review of literature
8-13
2.2 Gaps in literature review
14
2.3 Objective
14
CHAPTER: 3 EXPERIMENTAL SET-UP
vi
15-22
3.1 Investigational set-up
15
3.1.1 Machine Specifications
15
3.1.2 Workpiece specifications 3.1.2.1 Material Name 3.1.2.2 Physical Properties 3.1.2.3 Temper sorts
16-17 16 16-17 17
3.1.3 Tool Specifications
17-18
3.2 Selection of process parameters
18-20
3.3 Measurement of surface roughness
20-22
3.3.1 Amplitude parameters
20
3.3.2 Measurements
21-22
CHAPTER: 4 METHODOLOGY
23-32
4.1 Taguchi Method
23-24
4.2 Taguchi design methodology
24-27
4.2.1 Static problems
24-26
4.2.1.1 Smaller the better
25
4.2.1.2 Larger the better
25
4.2.1.3 Nominal the best
26
4.2.2 Dynamic problems
26-27
4.2.2.1 Sensitivity
27
4.2.2.2 Linearity
27
4.3 Steps of Taguchi methodology
27-28
4.4 Data Analysis
28-31
4.4.1 Minitab software
28-31
4.5 Advantages of Taguchi design
31-32
4.6 Disadvantages of Taguchi design
32
CHAPTER: 5 EXPERIMENTATION & ANALYSIS
5.1 Orthogonal array and L-9 matrix
33-44 33
5.2 Levels of control factors
33-36
5.3 Analysis
37-44
5.3.1 Interpretation
38-43
5.3.2 Conformation Experiments CHAPTER: 6 RESULTS AND CONCLUSION
43 45-46 47
REFERENCES vii
LIST OF FIGURES Fig. No.
Figure Description
Page No.
Figure 1.1
Schematic diagram of CNC
2
Figure 1.2
Point-to-point system
4
Figure 1.3
Contouring system
4
Figure 1.4
Contouring systems
4
Figure 3.1
Surface roughness tester
22
Figure 4.1
P-diagram for static problems
25
Figure 4.2
P-diagram for dynamic problems
26
Figure 5.1
Minitab Window
33
Figure 5.2
Turning Procedure
34
Figure 5.3
CNC CUB/XXZ servo controlled turret lathe
35
Figure 5.4
Plot for means
37
Figure 5.5
Plot for S/N proportion
40
Figure 5.6
Plot for means for MRR
41
Figure 5.7
Plot for S/N proportion for MRR
42
Figure 5.8
Turned workpieces(s)
44
viii
LIST OF TABLES Table No.
Table Description
Page No.
Table 3.1
Machine specifications
15
Table 3.2
Workpiece specifications
16
Table 3.3
Physical properties of workpiece
16-17
Table 3.4
Tool specification
17-18
Table 3.5
Insert specification
18
Table 5.1
Levels of control components
34
Table 5.2
Levels of control elements
34
Table 5.3
L9 orthogonal exhibit framework
35
Table 5.4
Result framework for surface roughness
36
Table 5.5
Result framework for Material Removal Rate
36
Table 5.6
Response table for means
39
Table 5.7
Response table for signal-to-noise ratio
40
Table 5.8
Response table for means
41
Table 5.9
Response table for signal-to-noise ratio
42
Table 5.10
Optimum table for surface roughness
43
Table 5.11
Optimum table for Material Removal Rate
43
Table 6.1
Analysis of Variance for Ra (µm), using adjusted SS
46
Table 6.2
Analysis of Variance for MRR (cc/min), using adjusted SS
46
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CHAPTER 1 INTRODUCTION 1.1 Background of CNC Today, Computer Numerical Control is an extension of what was once Numerical Control. It refers essentially to the concept of controlling automated machine tools via programmable computers. Clearly, with the older system of Numerical Control, a computer wasn’t involved, but today the technology has advanced in leaps and bounds (and continues to advance every year). CNC has set the stage for a tremendous upsurge in productivity – it’s an environment where machine tools can operate automatically, and without the attention and oversight of an operator. Historically, the first commercial Numerical Control machines were used in the early 1950’s, and operated with “punch tape”. And although a proven method, the so-called “new” technology was not readily accepted by manufacturers. In the late 1950’s, Numerical Control began to capture the interest of more and more manufacturers, but still with some problems and issues that required attention. Things became more manageable when industry groups standardized the operational aspects of NC, bringing some order and commonality to the manufacturing sector. Over the years, as CNC technology gained acceptance (with proven results), manufacturers began to replace older technologies and manual machining methods with Computer Numerical Control. And while the United States launched the CNC technology revolution, Germany and Japan became more successful in enhancing the technologies and bringing down unit costs. In more recent years, microprocessors have brought down unit costs even more, and have made CNC technology much more accessible to smaller manufacturing companies, as well as individuals. Whether it’s metal cutting machines, or woodworking machines, the technology is being used universally, and with advanced applications emerging every year. As for the CNC machinist, CAD programs, CAM programs, and other computer software are the basis for designing and fabricating almost
1
every product that consumers use on a daily basis. Indeed, like the 1950’s and 1960’s, advances and innovations in technology will continue to revolutionize throughout the 2000’s.
CNC 1.2 Introduction of CNC The term “CNC” is a generic term which can be used to describe many types of device, this would include plotters, vinyl cutters, 3D printers, milling machines and others. CNC stands for Computer Numerically Controlled and basically means that the physical movements of the machine are controlled by instructions, such as co-ordinate positions that are generated using a computer. The term “CNC Machine” is typically used to refer to a device which uses a rotating cutting tool which moves in 3 or more axes (X, Y and Z) to cut-out or carve parts in different types of materials. The information on these pages will focus on what are typically referred to as “CNC Routers” although it would be applicable to most CNC milling and engraving machines too.
Fig 1.1 Schematic 1.1 Schematic diagram of CNC [1]
2
CNC’s are incredibly versatile and allow you to cut a variety of different types of product and materials. The exact abilities of a machine will vary with size, rigidity and power. Typically most CNC Routers can cut soft and hard wood, plastics, other composites and non-ferrous metals. Signage, custom furniture, plaques, trophies, chocolate & cookie molds, awards, folk-art, toys, wall-hangings, plates & bowls, lithophanes, memorials, cabinets, doors, boxes, clocks, religious carvings, panel goods, architectural millwork & moldings, picture frames, mantels, archways, prototypes, remote control vehicle parts, face plates, heirloom gifts, military awards, education projects, movie & theatre props, coasters, chests, bottle totes, pool cues, rustic carvings and many other applications.
1.3 Basic Principle of CNC
Movements of X, Y, Z axis are controlled by motor which supplies either AC/DC.
Movement of machine is done by giving commands. co mmands.
All the operations are carried out by codes like spee d, feed, depth of cut, etc.
For each operation separate code is available.
Warning system is available to save guard the various operations and compone nts.
1.4
Classification of CNC
There are two main types of machine tools and the control systems required for use with them differ because of the basic differences in the functions of the machines to be controlled. They are known as point-to-point and contouring con trols. 1.4.1 Point-to-point systems : Some machine tools for example drilling, boring and
tapping machines etc., require the cutter and the work piece to be placed at a certain fixed relative positions at which they must remain while the cutter does its work. These machines are known as point-to-point machines and the control equipment for use with them are known as point-to-point control equipment. Feed rates need not to be programmed. In these machine tools, each axis is driven separately. In a point-to-point control system, the dimensional information that must be given to the machine tool will be a series of required position of the two slides. Servo systems can be used to move the slides and no attempt is made to move the slide until the cutter has been retracted.
3
1.4.2 Contouring systems (Continuous path systems) : Other type of machine tools
involves motion of work piece with respect to the cutter while cutting operation is taking place. These machine tools include milling, routing machines etc. and are known as contouring machines and the controls required for their control are known as contouring control. Contouring machines can also be used as point-to-point machines, but it will be uneconomical to use them unless the work piece also requires having a contouring operation to be performed on it. These machines require simultaneous control of axes. In contouring machines, relative positions of the work piece and the tool should be continuously controlled. The control system must be able to accept information regarding velocities and positions of the machines slides. Feed rates should be p rogrammed.
Fig 1.2 Point-to-point system [2]
Fig 1.3 Contouring system [2]
Fig 1.4 Contouring Systems [2]
4
1.5 Parameters of CNC There are various kinds of CNC machines used today. One of them is CNC turning machines, which are used to produce cylindrical parts. In these machines, a work piece keeps on rotating, and the cutting c utting tool moves in a linear fashion.
1.5.1 Cutting Parameters for CNC Turning Machines : Right cutting parameters produce a precise output, which helps in reducing cycle times, and machine costs. The speed and motion of the cutting tool are specified through several parameters that can be modified for different operations based upon the workpiece material and tool size. 1.5.1.1 Cutting speed : This criterion measures the number of feet the tool passes over the
surface of the work piece per minute in Surface Feet per Minute (SFM). The material and the process often decide the cutting speed. For example, grooving requires slower cutting speed for accurate results. 1.5.1.2 Cutting Feed : this parameter measure the distance undertaken by the cutting tool
for every single revolution. It is measured in Inches per Revolution (IPR). Depending upon the mode of operation, the tool is either fed into the workpiece or the workpiece is fed into the tool. 1.5.1.3 Spindle Speed : The spindle speed is obtained when the cutting speed is divided by
the circumference of the work piece in Revolutions per Minute (R.P.M). The speed varies depending upon several factors like the diameter o f the cut or the surface area. 1.5.1.4 Feed Rate : It is defined as the speed of the cutting tool when it cuts through the
material. It is the product of the cutting and spindle speed measured in Inches per Minute (IPM). c uts towards 1.5.1.5 Axial Depth of Cut : This parameter measures the depth of a tool as it cuts the axis of the material. A large axial depth of cut is needed to overcome the high load on the tool.
5
1.5.1.6 Radial Depth of the Cut : This parameter measures the depth of the tool as it cuts
along the radius of the material. To enhance the quality of the cutting tool, a lower feed rate is needed.
1.6 Advantages of CNC 2
CNC machines can be used continuously 24×7 throughout the year and only need to be switched off for occasional maintenance.
3
CNC machines are programmed with a design which can then be manufactured hundreds or even thousands of times. Each manufactured product will be exactly the same.
4
Less skilled/trained people can operate CNC machines unlike manual lathes milling machines etc. which need skilled engineers.
5
CNC machines can be updated by improving the software used to drive the
machines
6
Training for correct use of CNC machines is available through the use of ‘virtual software’. This software is like a computer game that allows the operator to practice using the CNC machine on the screen of a computer.
7
Modern design software allows the designer to simulate the manufacture of his/her idea. There is no need to make a prototype or a model. This saves time and
money.
8
One person can supervise many CNC machines as once they are programmed they can usually be left to work by themselves. Only the cutting tools need replacement occasionally.
1.7 Limitations of CNC
CNC machines are more expensive than manually operated machines, although costs are slowly coming down.
The CNC machine operator only needs basic training and skills, enough to supervise several machines. In years gone by, engineers needed years of training to operate center lathes, milling machines and other manually operated machines. This means many of the old skills are being lost. 6
Fewer workers are required to operate CNC machines compared to manually operated machines. Investment in CNC machines can lead to unemployment.
Many countries no longer teach pupils / students how to use manually operated lathes / milling machines etc… Pupils / students no longer develop the detailed skills required by engineers of the past. These include mathematical and engineering skills.
1.8 Applications of CNC
Metal Fabrication
Wood Manufacturing
Computer Parts and Components Manufacturing
Electrical Industry
Plastics Manufacturing
Precise Component Machining
Range of Materials
Repeatability
Higher flexibility
Increased productivity
Consistent quantity
Reduced scrap rate
Reliable operation
Reduced nonproductive time
Reduced manpower
Shorter cycle time
Higher accuracy
Reduced lead time
Just In time (JIT) manufacture
Automatic material handling
Lesser floor space
Increased operational safety
7
CHAPTER 2 LITERATURE REVIEW 2.1
Review of Literature
Over the years a lot of research work has been done over the working of a Computer Numeric Control machine and optimization of its process parameters in order to have a controlled and feasible output. Here is some of the research works done under explained as the literature review . Prajapati, K. et al [1] have optimized the machining parameters for SR and MRR in
CNC turning. SS 316 (austenite steel) work material of Ø 45 mm and length 35 mm was used in turning in dry environment conditions. In this study, the effect and optimization of machining parameters (cutting speed, feed rate and depth of cut) on SR and MRR is investigated. An L27 Orthogonal array, analysis of variance (ANOVA) and grey relation analysis is used. The percentage contribution of cutting speed is 5.29 %, feed of 86.13 % and depth of cut of 3.27 % on surface roughness. From the ANOVA it is conclude that the feed rate is most significant parameter which contributes more to surface roughness. In multi response optimization the optimum parameter combination is meeting at experiment 3 and its parameter value is 1.4 mm depth of cut, 125 m/min cutting speed and 0.1 mm/rev feed rate. From the ANOVA it is conclude that the depth of cut is most significant parameter which contributes more to material removal rate.
Zhang, Julie, Z. et al [2] investigated the Taguchi design application to optimize surface
quality in a CNC face milling operation. An orthogonal array of L9 was used and ANOVA analyses were carried out to identify the significant factors affecting surface roughness. CNC Mill: Fadal VMC-40 vertical machining center was used for this experiment and 19.1×38.1×76.2 mm aluminum blocks as a work piece. The experimental results indicate that in this study the effects of spindle speed and feed rate on surface were larger than depth of cut for milling operation. In this study the optimal cutting condition for face milling was selected by varying cutting parameters through the 8
Taguchi parameter design method. With the L9(34) orthogonal array, a total of 36 experimental runs, covering three main factors each at three levels and two noise factors each at two levels, indicated that the Taguchi parameter design was an efficient way of determining the optimal cutting parameters for surface finish. The experimental results indicate that in this study the effects of spindle speed and feed rate on surface were larger than depth of cut for milling operation. In addition, one of the noise factors, tool wear, was found to be statistically significant. The surface finish achievement of the confirmation runs under the optimal cutting parameters indicated that of the parameter settings used in this study, those identified as optimal through Taguchi parameter design were able to produce the best surface roughness in this milling operation. This was accomplished with a relatively small number of experimental runs, given the number of control and noise factors, suggesting that Taguchi parameter design is an efficient and effective method for optimizing surface roughness in a milling operation.
Joshi, A. et al [3] investigated the SR response on CNC milling by Taguchi technique.
Analysis of variance (ANOVA) was used in this investigation. The material used for the experiment is (100 x 34 x 20 mm) 5 blocks of aluminum cast heat-treatable alloy. The output characteristic, surface finish is analysed by software Minitab 15 and ANOVA is formed, which shows the percentage contribution of each influencing factor on surface roughness. CNC End milling is a unique adaption of the conventional milling process which uses an end mill tool for the machining process. CNC Vertical End Milling Machining is a widely accepted material removal process used to manufacture components with complicated complicated shapes and profiles. During the End milling process, process, the material is removed by the end mill cutter. The effects of various parameters of end milling process like spindle speed, depth of cut, feed rate have been investigated to reveal their Impact on surface finish using Taguchi Methodology. Experimental plan is performed by a Standard Orthogonal Array. The results
of analysis of variance
(ANOVA) indicate that the feed Rate is most influencing factor for modeling surface finish. The graph of S-N Ratio indicates the optimal setting of the machining parameter which gives the optimum value of surface finish. The optimal set of process parameters has also been predicted to maximize the surface finish. 9
Reddy, B. et al [4] Pre-hardened steel (P20) is a widely used material in the production
of molds/dies due to less wear resistance and used for large components. In this study, minimization of surface roughness has been investigated by integrating design of experiment method, Response surface methodology (RSM) and genetic algorithm. To achieve the minimum surface roughness optimal conditions are determined. The experiments were conducted using Taguchi’s L50 orthogonal array in the design of experiments (DOE) by considering the machining parameters such as Nose radius (R), Cutting speed (V), feed (f), axial depth of cut (d) and radial depth of cut(rd). A predictive response surface model for surface roughness is developed using RSM. The response surface (RS) model is interfaced with the genetic algorithm (GA) to find the optimum machining parameter values. In this study, an efficient optimization methodology using RSM and GA is introduced in minimizing surface roughness of P20 mold steel in CNC end milling process. To achieve the minimum surface roughness, the appropriate process parameters are determined. Nose radius, cutting speed, feed rate, axial depth of cut and radial depth of cut are considered as process parameters. A predictive model for surface roughness is created in terms of the process parameters using RSM to increase the quality of the surface finish. The RSM model is interfaced with an effective GA to find the optimum process parameter values. GA has reduced the surface roughness of the initial model significantly. Surface roughness is improved by about 44.22%. Kromanis, A. et al [5] studied to develop a technique to predict a surface roughness of
part to be machined. 3D surface parameters give more precise picture of the surface; therefore it is possible more precisely to evaluate the surface parameters according to technological parameters. In result of the study, the mathematical model of end-milling is achieved and qualitative analysis is maintained. Achieved model could help technologists to understand more completely the process of forming surface roughness. Pre-hardened steel (P20) is a widely used material in the production of molds/dies due to less wear resistance and used for large components. In this study, minimization of surface roughness has been investigated by integrating design of experiment method, Response surface methodology (RSM) and genetic algorithm. To achieve the minimum surface roughness optimal conditions are determined. The experiments were conducted using Taguchi’s L50 orthogonal array in the design of experiments (DOE) by considering the 10
machining parameters such as Nose radius (R), Cutting speed (V), feed (f), axial depth of cut (d) and radial depth of cut (rd). A predictive response surface model for surface roughness is developed using RSM. The response surface (RS) model is interfaced with the genetic algorithm (GA) to find the optimum machining parameter values. Bajic, D. et al [6] this paper focuses on surface morphology of machined brass reinforced
epoxy composite with different particle sizes in computer numerical control (CNC) milling process. Morphological studies in this research contain composite’s surface roughness and dispersion of brass particles. Surface roughness is a proper criterion for predicting the performance of machining parameters and an d the quality of products. In this experiment, cutting parameters evaluated are feed rate, spindle speed, and depth of cut. Scanning electron microscope (SEM) and surface roughness measurement are carried out to study the major changes in texture of the machined surface and determine the optimal mixed-level array of cutting parameters. The results indicate that these parameters have significant effects on surface roughness. In the other word, a better surface quality can be obtained by varying the level of cutting parameters. In addition, it is concluded that a better surface roughness would be achieved by using the smallest size size of brass particles. Chockalingam, P. et al [7] studied the effect of different coolant conditions on milling of
AISI 304 stainless steel. Cooling methods used in this investigation were flooding of synthetic oil, water-based emulsion, and compressed cold air. Cutting forces and the surface roughness were studied and tool flank wears observed. In this study, the comparison between different coolants effect to the milling of AISI 304 stainless steel is done. This research deals with the effect of different coolant conditions on milling of AISI 304 stainless steel. Cooling methods used in this investigation were flooding of synthetic oil, water-based emulsion, and compressed cold air. Cutting forces and the surface roughness were studied and tool flank wears observed. In this study, the comparison between different coolants’ effect to the milling of AISI 304 stainless steel is done and the results from the study can provide very useful information in manufacturing field. The experiment results showed that water-based emulsion gave better surface finish and lower cutting force followed by synthetic oil and compressed cold air. Different cooling condition required different parameters in order to obtain lower surface 11
roughness and cutting force. Chipping was the initial wear mode in the milling of AISI 304 stainless steel. Rajput, R. et al [8] studies that the quality of finished work piece depends on the relative
positions between the work pieces, cutting tool, machining process proce ss parameters. It can be achieved if a CNC machine tool possesses sufficient strength to withstand the cutting forces, stiffness against deformation and capability of CNC controller. CNC controller is the heart of the CNC machine which controls most of the functions of CNC machine. Accurate and Perfect machining in minimum time is the requirement of manufacturing industries and along with other hardware and machining process parameters, CNC control system also playing vital and an important role. Hence, in this work an attempt is being made to investigate and analyze the comparison of the CNC milling controllers with same set of parameters. This project gives the detailed comparison of the three major CNC controllers used by industries on the basis of important parameter. Overall the research study reveals that the different–different CNC controllers have the different features. This research study conclude that for the given geometry, the result are better for the HEIDENHAIN 426 TNC and SINUMERIK 840D milling controller as compare to the FANUC21M for the given geometry or job some more important result are concluded during this work .Different Controllers are having different capabilities, so we need to identify the right controller for right job that can minimize the machining time and ultimately optimize the associated parameters. Average processing time for the HEIDENHAIN TNC 426 is minimum as compared to other two controllers. In some observations, the results of SINUMERIK 840D are very near to the HEIDENHAIN TNC 426. MRR is found dependent on the controller features. During this work MRR is found high for the H TNC 426, than compared low for the S 840D and lowest for the F 21 M. Sureshkannan, G. et al [9] analyzes the effects of material properties on surface
roughness, material removal rate, and tool wear on high speed CNC end milling process with various ferrous and nonferrous materials. The challenge of material specific decision on the process parameters of spindle speed, feed rate, depth of cut, coolant flow rate, cutting tool material, and type of coating
12
for the cutting tool for required quality and quantity of production is addressed. Generally, decision made by the operator on floor is based on suggested values of the tool manufacturer or by trial and error method. This paper describes effect of various parameters on the surface su rface roughness characteristics of the th e precision p recision machining part. The prediction method suggested is based on various experimental ex perimental analyses of parameters in different compositions of input conditions which would benefit the industry on standardization of high speed CNC end milling processes. The high speed CNC machining is a vital and costly machining process and a less harder material in ductile class would yield good surface finish whereas the closer variation in brittle class, but less porous brittle material, has good surface finish with higher depth of cut and feed rate and spindle speed more than the mid value, which improves productivity.
Makwana, R., D. et al [10], a fixture is designed and built to hold, support and locate
every component to ensure that each is drilled or machined with accuracy and manufactured individually. The fixture designing and manufacturing is considered as complex process that requires the knowledge of different areas, such as geometry, dimensions, tolerances, procedures and manufacturing processes. This paper will give brief overview about the 3-2-1 locating principle to design the fixture for complex parts and other clamping principles. This paper also gives the idea and procedure for fixture design. This paper gives the idea about the modular fixture and dedicated fixture. From the study they concluded that for designing the fixture the geometry method (3-2-1 principle) is very v ery useful for the complex component having various machining processes though it is the basic principle of the fixture design.
13
2.2
Gaps in Literature Review
After a comprehensive study of the existing literature review, a number of gaps have been observed in machining of CNC
Most of the researchers have investigated influence of a limited number of process parameters on the performance measures of CNC CNC parts.
Literature review reveals that the researchers have carried out most of the work on CNC developments, monitoring and control but very limited work has been reported on optimization of process variables.
The effect of machining parameters on Aluminium alloy has not been fully explored using CNC.
Multi-response optimization of CNC process is another thrust area which has been given less attention in past studies.
2.3
Objective
The main objective of this project is the development of the data model for the interface of the interface of CNC for turning. The technology grinding was excluded because a first study showed that a lot of manual interactions hamper the automatic flow and that the process dependency of data is very high.
The developed interface for the other technologies should be tested by prototype implementation at the CAM and the CNC level.
The prototypes should be tested and validated at real machines with suitable workpiece(s). After the validation the data model should be fixed in an international standard.
14
CHAPTER 3 EXPERIMENTAL SET-UP 3.1 Investigational Set-up Numerical control (NC) is a system for naturally working an assembling machine in view of a code letters, numbers and exceptional characters. The numerical information needed to create a section is given to a machine as system, called part program or CNC (PC numerical control). The system is interpreted into the proper electrical signs for info to engines that run the machine. A CNC machine is a numerical control machine with the included component of a board PC. The PC is alluded to as the machine control unit (MCU).
3.1.1 Machine Specifications Specifications : A servo controlled CNC 3-axes XXZ Turret lathe is used for turning ope ration, operating on a DC power source and having 8 tool inserting positions as a part of the turret. Table 3.1 Machine Specifications
Machine type
CUB/XXZ
Machine number
708
Year
2007
Supply voltage
380V/415V
Control voltage
24V D.C.
Back up fuse
32 AMPS
Rated current
21/20 AMPS
KVA rating
14 KVA
Size of wire
6 SQ mm
Turret Profile
SERVO TURRET
15
3.1.2 Workpiece Specifications: An Aluminium alloy has been selected as the workpiece of choice to perform turning operation with the aimed objective of the project. It is Aluminium 6063-T6 alloy have Aluminum, Magnesium and silicon as it major constituent. 3.1.2.1 Material name - Aluminium 6063-T6/UNS A96063/ISO AlMg0.5Si Table 3.2 Workpiece specifications
Components
Wt.%
Al
Max 97.5
Cr
Max 0.1
Cu
Max 0.1
Fe
Max 0.35
Mg
0.45-0.9
Mn
Max 0.1
Si
0.2-0.6
Ti
Max 0.1
Zn
Max 0.1
Other, each
Max 0.05
Other, total
Max 0.15
3.1.2.2 Physical properties
Apart from its metallurgical characteristics, the physical p roperties of Aluminium 6063 T6 alloy is also having a major role in calculating the values of various responses one can obtain from a machining operation using a CNC turret lathe. Table 3.3 Physical Properties of workpiece
Hardness, Brinell
79
Hardness, Knoop
96
Hardness, Vickers
83
Ultimate tensile strength
241 MPa
16
Tensile yield strength
214 MPa
Elongation at break
12%
Modulus of elasticity
68.9 GPa
Ultimate bearing strength
434 MPa
Bearing yield strength
276 MPa
Poisson’s ratio
0.33
Fatigue strength
68.9 MPa
Machinability
70%
Shear modulus
25.8 GPA
Shear strength
152 MPa
3.1.2.3 Temper sorts : The most well-known tempers for 6082 aluminum are:
•
T6 - Solution warmth treated and misleadingly matured
•
0-Soft
•
T4 - Solution warmth warmth treated treated and normally matured to a considerably stable condition
•
T651 - Solution warmth warmth treated, treated, anxiety assuaged by extending then falsely falsely matured matured
3.1.3 Tool Specifications Tool material- Tool Steel Tool holder specification- DVJNR 2020k 16 Table 3.4 Tool Holder Specifications
Tool cutting edge angle
93 deg
Tool lead angle
-3 deg
Maximum ramping angle
44 deg
Machine side body angle
0 deg
17
Workpiece side body angle
0 deg
Orthogonal rake angle
-4 deg
Inclination angle
-13 deg
Shank height
20 mm
Shank width
20 mm
Functional length
125 mm
Functional width
25 mm
Functional height
20 mm
Maximum overhang
46.6 mm
Torque
3 Nm
Weight
0.426 kg
Body material code
Steel
Tool Insert- VNMG 160408 Table 3.5 Insert Specifications
Grade
4325
Coating
CVD Ti(C,N) + Al2O3 +TiN
Clearance angle
0 deg
Weight
0.01 kg
3.2 Selection of Process Parameter The three key mechanical inputs in metal evacuation operations are bolster, rate, and profundity of cut. Controlling the food, rate and profundity of cut can amplify the advantages of a specific cutting liquid and can expand efficiency. Be that as it may, as most choices, the decision of food, pace and profundity of cut must be in light of the client's targets. What is their objective in this application? Would they like to make parts quicker or augment instrument life? How critical is the surface completion and dimensional exactness of the part? Answers to these inquiries will drive their choices on bolsters, velocities and profundity of cut.
18
Some of the process parameters are
Speed : Speed is the rate of turn of the axle where the device is held. It is measured in cycles every moment (RPMs).
Feed : Feed is the rate at which the instrument is moved into the part or the part into the device. Food is measured in feet, inches or millimeters per time period.
Depth of Cut (DOC): The estimation typically in inches or millimeters) of how wide and profound the apparatus cuts into the work piece. Speed, food and DOC
Tool life
Surface wrap up
Dimensional precision of the produced part
Power needed by the machine instrument
In turning, the rate and movement of the slicing device is determined through a few parameters. These parameters are chosen for every operation based b ased upon u pon the t he work piece piec e material, instrument material, device size, and that's just the beginning. Turning parameter that can influence the procedure is:
- The rotational velocity of the shaft and the work piece in cycles Spindle speed every moment (RPM). The axle pace is equivalent to the cutting rate partitioned by the perimeter of the work piece where the cut is being made. With a specific end goal to keep up a steady cutting speed, the shaft speed must shift taking into account the breadth of the cut. In the event that the shaft pace is held consistent, then the cutting rate will change.
Feed rate rate - The rate of the slicing apparatus' development in respect to the work piece as the instrument makes a cut. The food rate is measured in millimeter per unrest (RPM).
Tool life: For the most part, expanding the food rate lessens apparatus life. Uprooting more material makes more warmth. Warmth debases the work piece and the tooling. On the off chance that you lessen your food rate, the instrument life enhances on the grounds that it is not functioning as hard.
19
Surface finish: It might be justified, despite all the trouble to the client to build the expense of the coolant (with a somewhat higher fixation or higher lubricate liquid) to enhance the surface completion at higher paces. Obviously, this implies that surface completion must be worth something to yours client. Expanding the DOC can advance prattle as a result of higher strengths. The machine device must be sufficiently inflexible to withstand these powers. Vibrations in the machine apparatus can have an expansive impact on surface completion. Machine apparatuses composed and built for rapid machining have enhanced solidness to minimize the vibration that effects surface completion.
Material Removal Rate: It can be defined as the amount of material removed per unit machining time. For a turning operation it can also be mathematically depicted as the product of feed rate, depth of cut and cutting speed. That is, MRR = Feed x Depth of Cut x Cutting Speed……………………………….(F1) Speed……………………………….(F1)
3.3 Measurement Of Surface Roughness Surface unpleasantness, regularly abbreviated to harshness, is a measure of the composition of a surface. It is evaluated by the vertical deviations of a genuine surface from its optimal structure. On the off chance that these deviations are expansive, the surface is harsh; in the event that they are little the surface is smooth. Harshness is ordinarily thought to be the high recurrence, short wavelen gth part of a deliberate surface.
3.3.1 Amplitude parameters- Sufficiency parameters portray the surface in light of the vertical deviations of the harshness profile from the mean line. A considerable lot of them are firmly identified with the parameters found in insights for describing populace tests. For instance, Ra is the number juggling normal of the outright values and Rt is the scope of the gathered harshness information focuses. The normal harshness, Ra, is communicated in units of tallness. In the Imperial (English) framework, 1 Ra is normally communicated in "millionths" of an inch. This is additionally alluded to as "small scale crawls" or once in a while generally as "miniaturized scale". The plentifulness parameters are by a long shot the most well-known surface unpleasantness parameters found in the United States on mechanical designing drawings and in specialized writing.
20
3.3.2
Measurement - The accompanying instruments are mechanically utilized for
measuring surface unpleasantness. There are numerous makers executing these innovations into items,
Profilometer, customarily called a stylus and works like a phonograph
Atomic power magnifying instrument.
Surface Roughness Tester
Roughness is an important parameter when trying to find out whether a surface is suitable for a certain purpose. Rough surfaces often wear out more quickly than smoother surfaces Rougher surfaces are normally more vulnerable to corrosion and cracks, but they can also aid in adhesion. A roughness tester is used to quickly and accurately determine the surface texture or surface roughness of a material. A roughness tester shows the m easured roughness depth (Rz) as well as the mean roughness value (Ra) in micrometers or microns (µm). Measuring the roughness of a surface involves applying a roughness filter. Different international standards and surface texture or surface finish specifications recommend the use of different roughness filters. For example, a Gaussian filter often is recommended in ISO standards. Roughness tester is an ideal instrument for fast and simple checking of the surface roughness in shop floor, metalworking, manufacturing, quality control, inspection, automotive and aerospace engineering. It is a portable and pocket-sized instrument, which provides you with highly accurate measurements of surface finish. This instrument is compatible with four standards of site to measure surface roughness of various machinery-processed parts, calculate corresponding and clearly display all measurement parameters. When measuring the roughness of a surface, the sensor is placed on the surface and then uniformly slides along the surface by driving the mechanism by the sharp built-in probe. This roughness causes displacement of the probe which results in change of inductive amount of induction coils so as to generate analogue signal, which is in proportion to the surface roughness at output end of phase-sensitive rectifier.
21
The exclusive DSP processes and calculates and then outputs the measurement results on LCD.
Multiple parameter measurement: Ra, Rz, Rq, Rt
Highly sophisticated inductance sensor
Four wave filtering methods : RC, PC-RC, GAUSS and D-P
Built-in lithium ion rechargeable battery and control circuit with high capacity
Can communicate with PC computer for statistics, printing and analysing by the optional cable and the software for RS232C interface.
Manual or automatic shutdown. The tester can be switched off by pressing the Power key at any time.
On the other hand, the tester will power itself off about 5 minutes after the last key operation.
Fig 3.1 Surface Roughness Tester [3] 22
CHAPTER 4 METHODOLOGY 4.1 Taguchi Method Dr. Taguchi of Nippon Telephones and Telegraph Company, Japan has developed a method based on “ORTHOGONAL ARRAY” experiments which gives much reduced “variance” for the experiment with “optimum settings” of control parameters. Thus the marriage of Design of Experiments with optimization of control parameters to obtain BEST results is achieved in the Taguchi Method. "Orthogonal Arrays" (OA) provide a set of well balanced (minimum) experiments and Dr. Taguchi's Signal-to-Noise ratios (S/N), which are log functions of desired output, serve as objective functions for optimization, help in data analysis and prediction of optimum results. Numerous Japanese firms made awesome progress by applying his routines. Taguchi has gotten a percentage of the Japan's most prestigious honors for quality accomplishment, including the Deming Prize. Pignatiello has recognized two unique parts of Taguchi method.
The method of Taguchi
Strategies of Taguchi procedure is the theoretical casing work for arranging a procedure or item outline test. Taguchi strategies allude to the gathering of particular systems utilized by Taguchi. Taguchi has tended to Design, Engineering (disconnected from the net) and also Manufacturing (online) quality. This idea separates Taguchi strategy from Statistical Process Control (SPC) which is absolutely an online quality control method.
Taguchi thoughts can be lessened into two major ideas.
Quality misfortunes ought to be characterized as deviation from target, not conformance to self-assertive details.
23
To accomplish high framework quality levels financially obliges quality to be outlined into item. Quality is outlined, not no t fabricated, into the item. Taguchi strategies speak to rationality. Quality is measured by the deviation of a useful trademark from its objective worth. Clamors (wild components) will bring about such deviations which bring about loss of Quality. Taguchi procedures try to evacuate the impact of Noises. The most essential piece of the Taguchi strategy is quality misfortune capacity. Taguchi has observed that a quadratic capacity (parabola) approximates the conduct of misfortune by and large. When the quality normal for hobby is to be expanded or minimized, the misfortune capacity will turn into a half parabola. Loss happens not just when the item is outside its determination additionally when item falls inside of its detail. Taguchi has prescribed sign to commotion proportion (S/N proportion) as execution insights. Sign alludes to the adjustment in quality attributes of an item under scrutiny in light of an element presented in the trial configuration. Clamor alludes to the impact of outside variables (wild parameters) on the result of the quality attributes.
4.2 Taguchi Design Methodology Taguchi Method treats optimization problems in two categories,
4.2.1
Static Problems- Generally, a process to be optimized has several control
factors which directly decide the target or desired value of the output. The optimization then involves determining the best control factor levels so that the output is at the the target value. Such a problem is called as a "STATIC PROBLEM". This is best explained using a P-Diagram which is shown below ("P" stands for Process or Product). Noise is shown to be present in the process but should have no effect on the output! This is the primary aim of the Taguchi experiments - to minimize variations in output even though noise is present in the process. The process is then said to have become ROBUST.
24
Fig 4.1 P-diagram for static problems [4]
There are 3 Signal-to-Noise ratios of common interest for optimization of Static Problems; 4.2.1.1
Smaller The Better-
n = -10 Log10 [mean of sum of squares of measured data]………… ……………(F2) This is usually the chosen S/N ratio for all undesirable characteristics like " defects " etc. for which the ideal value is zero. Also, when an ideal value is finite and its maximum or minimum value is defined (like maximum purity is 100% or maximum Tc is 92K or minimum time for making a telephone connection is 1 sec) then the difference between measured data and ideal value is expected to be as small as possible. The generic form of S/N ratio then becomes, n = -10 Log10 [mean of sum of squares of {measured - ideal}]………………… id eal}]……………………….(F3) …….(F3) 4.2.1.2 Larger The Better-
n = -10 Log10 [mean of sum squares of reciprocal of measured data]………………… da ta]…………………(F4) (F4) This case has been converted to SMALLER-THE-BETTER by taking the reciprocals of measured data and then taking the S/N ratio as in the smaller-the-better case.
25
4.2.1.3 Nominal The Best-
n = 10 Log10 (square of mean/variance)…………………… mean/variance)………………………………………… …………………………(F5) ……(F5) This case arises when a specified value is MOST desired, meaning that neither a smaller nor a larger value is desirable.
4.2.2
Dynamic Problems- If the product to be optimized has a signal input that
directly decides the output, the optimization involves determining the best control factor levels so that the "input signal / output" ratio is closest to the desired relationship. Such a problem is called as a "DYNAMIC PROBLEM". This is best explained by a P-Diagram which is shown below. Again, the primary aim of the Taguchi experiments - to minimize variations in output even though noise is present in the process- is achieved by getting improved linearity in the input/output relationship.
Fig 4.2 P-diagram for dynamic problems [4]
In dynamic problems, we come across many applications where the output is supposed to follow input signal in a predetermined manner. Generally, a linear relationship between "input" "output" is desirable. There are 2 characteristics of common interest in "followthe-leader" or "Transformations" type of applications,
26
(i)
Slope of the I/O characteristics
(ii)
Linearity of the I/O characteristics (minimum deviation from the best-fit straight
line)
The Signal-to-Noise ratios for these 2 characteristics have been defined as, 4.2.2.1
Sensitivity- The slope of I/O characteristics should be at the specified
value (usually 1). It is often treated as Larger-The-Better when the output is a desirable characteristics (as in the case of Sensors, where the slope indicates the sensitivity). n = 10 Log10 [square of slope or beta of the I/O characteristics]………(F6) characteristics]………(F6) On the other hand, when the output is an undesired characteristic, it can be treated as Smaller-the-Better. n = -10 Log10 [square of slope or beta of the I/O characteristics] 4.2.2.2
Linearity- Most dynamic characteristics are required to have direct
proportionality between the input and output. These applications are therefore called as "TRANSFORMATIONS". The straight line relationship between I/O must be truly linear i.e. with as little deviations from the straight line as possible. p ossible. n= 10Log10 (Square
of
slope or
beta/variance)…………..(F7)
Variance in this case is the mean of the sum of squares of deviations of measured data points from the best-fit straight straight line (linear regression).
4.3
Steps in Taguchi Methodology-
Taguchi method is a scientifically disciplined mechanism for evaluating and implementing improvements in products, processes, materials, equipment, and facilities. These improvements are aimed at improving the desired characteristics and simultaneously reducing the number of defects by studying the key variables controlling the process and optimizing the procedures or design to yield the best results. results. The method
27
is applicable over a wide range of engineering fields that include processes that manufacture raw materials, sub systems, products for professional and consumer markets. Taguchi proposed a standard 8-step procedure for applying his method for optimizing any process, 1.
IDENTIFY THE MAIN FUNCTION,SIDE EFFECTS, AND FAILURE MODE
2.
IDENTIFY THE NOISE FACTORS,TESTING CONDITIONS, AND Q UALITY
CHARACTERISTICS
3.
IDENTIFY THE OBJECTIVE FUNCTION TO BE OPTIMIZED
4.
IDENTIFY THE CONTROL FACTORS AND THEIR LEVELS
5.
SELECT THE ORTHOGONAL ARRAY MATRIX EXPERIMENT
6.
CONDUCT THE MATRIX EXPERIMENT
7.
ANALYZE THE DATA,PREDICT THE OPTIMUM LEVELS AND PERFO
RMANCE
8.
PERFORM THE VERIFICATION EXPERIMENT &
ACTION
4.4
Data Analysis-
PLAN
THE
FUTURE
4.4.1 Minitab Software- Minitab is a measurable Analysis programming that permits to effortlessly lead examinations of information. This is one of the proposed programming for the class. This aide is expected to guide you through the essentials of of Minitab and help you begin with it. Utilizing Minitab as a part of Harper and Gleacher Centre. Minitab can be found in the PC lab PCs. With a specific end goal to stack the product go to: Begin - > PROGRAMS - > MATH and STATS - > MINITAB 15 - > MINITAB 15 STATISTICAL SOFTWARE ENGLISH Minitab has two fundamental sorts of records, undertakings and worksheets. Worksheets are documents that are comprised of information; think about a spread sheet containing 28
variables of information. Undertakings are comprised of the summons, charts and worksheets. Each time you spare a Minitab venture you will be sparing charts, worksheets and orders. However every one of the components can be spared separately for utilization in different records or Minitab ventures. Similarly you can print ventures and its components. Minitab records are sorted out as "undertakings". Every venture will contain all the information you utilize and the charges and examination you perform on the information. You can open another, unfilled worksheet whenever. In this unfilled worksheet you can duplicate, glue and sort the information you require by just chipping away at the worksheet as you would on any spread sheet. Approaches Approaches To Analyze Data- Examination in Minitab should be possible in two ways:
utilizing the Built-As a part of schedules or utilizing charge dialect as a part of the Session window. These two can be utilized conversely. Most of the capacities required is essential and more progressed measurable investigation are found as Minitab Built-in schedules. These schedules are gotten to through the menu bar. To utilize the menu orders, click on a thing in the menu bar to open a menu, click on a menu thing to execute a charge or open a submenu or dialog box. Charge Language: To have the capacity to sort summons in the Session window, you must acquire the "MTB>" brief. All orders are then entered after the FALL 2009 BUSINESS STATISTICS 41000 GUIDE TO MINITAB 15 9 "MTB>" brief. All order lines are free arrangement, at the end of the day, all content may entered in upper or lowercase letters anyplace in the line. Descriptively, To acquire spellbinding insights of a variable or set of variables, go to •
Details - > DISPLAY DESCRIPTIVE STATISTICS
•
Furthermore, a brief window ought to show up. In the window select the variable(s) you need to investigate and click alright.
•
Results will be displayed in the Session window as takes after.
29
Chart attracting assembled schedules in Minitab can be found under the GRAPH menu in the menu bar. On the Graph Menu you have a few sorts of plots that you can look over, and that you can use to deliver your coveted plot. The following is a clarification of how to utilize the most well-known chart schedules. Minitab charts will show up as particular windows that are considered piece of the venture, on the other hand they can be spared and replicated for utilization in reports. Plots can be altered by adjusting the plot choices. With a specific end goal to figure essential insights for sets of variables, similar to covariance and connection, go to STAT - > BASIC STATISTICS In this record cases of measurements are computed accepting just two variables are being examined, however combine savvy measurements for more than two variables can be ascertained by basically adding all the craved variables to the "VARIABLES" enclose the dialog windows. •
Pick COVARIANCE to acquire the accompanying dialog box. Pick the pair of variables you wish to examine and click OK.
•
The outcome will be exhibited in the Session window as introduced beneath.
•
Pick CORRELATION and acquire the accompanying dialog box. Pick the pair of variables to be investigated.
•
Results are shown in the Session window as displayed beneath. Adding variables
•
To include variables name the variable where you need to store the outcomes.
•
Select the first variable, press the "+" sign and select the second variable (thus on for more than two variables). You ought to acquire something like the window in the privilege
•
The outcome will then be indicated in the worksheet window Taking logarithms another helpful capacity in factual investigation is to take logs of variables.
30
•
Search for the "Characteristic LOG" or "LOG BASE 10" (contingent upon the one you require) in the capacity list. An alternate way to discovering the capacities is to pick "LOGARITHM" from the capacity drop down menu.
•
Inside the bracket, change number for the variable name. Highlighting "NUMBER" and after that selecting the variable you n eed to change does this.
•
Verify that you have characterized a variable where you need to store results, by putting the name in the "STORE RESULT IN VARIABLE" box.
•
The outcome will show up in the worksheet window. Logical capacities Some measurable investigation should separate by gatherings as indicated by qualities that are contained in the information. Consistent capacities are especially helpful in these cases. A straightforward case on the best way to utilize them is portrayed underneath.
•
Pick the variable you need to do the sensible test to. Here we are taking a gander at the variable.
•
Pick the sensible test you need to utilize. Here we need to see which perceptions have the variable equivalent.
•
Verify that you have shown a variable in which to store your outcomes, by writing the name of your outcome variable in the "STORE RESULT IN VARIABLE" box.
•
The outcome variable will be a double (variable of 1s and 0s)
4.5 Advantages of Taguchi Design1.
It emphasizes a mean performance characteristic value close to the target value rather than a value within certain specification limits, thus improving the product
quality.
31
2.
Additionally, Taguchi's method for experimental design is straightforward and easy to apply to many engineering situations, making it a powerful yet simple
tool.
3.
It can be used to quickly narrow down the scope of a research project or to identify problems in a manufacturing process from data already in existence.
4.
Also, the Taguchi method allows for the analysis of many different parameters without a prohibitively high amount of experimentation. For example, a process with 8 variables, each with 3 states, would require 6561 (38) experiments to test all variables.
5.
However using Taguchi's orthogonal arrays, only 18 experiments are necessary, or less than .3% of the original number of experiments. In this way, it allows for the identification of key parameters that have the most effect on the performance characteristic value so that further experimentation on these parameters can be performed and the parameters that have little effect can be ignored.
4.6 Disadvantages Of Taguchi Design1.
The main disadvantage of the Taguchi method is that the results obtained are only relative and do not exactly indicate what parameter has the highest effect on the performance characteristic value. Also, since orthogonal arrays do not test all variable combinations, this method should not be used with all relationships between all variables are needed.
2.
The Taguchi method has been criticized in the literature for difficulty in accounting for interactions between parameters.
3.
Another limitation is that the Taguchi methods are offline, and therefore inappropriate for a dynamically changing process such as a simulation study. Furthermore, since Taguchi methods deal with designing quality in rather than correcting for poor quality, they are applied most effectively at early stages of process development. After design variables are specified, use of experimental design may be less cost effective.
32
CHAPTER 5 EXPERIMENTATION EXPERIMENTATION AND ANALYSIS
5.1
Orthogonal Array And L-9 Matrix-
To choose a suitable orthogonal exhibit for analyses, the aggregate degrees of opportunity should be processed. The degrees of opportunity are characterized as the quantity of examinations between procedure parameters that should be made to figure out which level is better and particularly how much better it is. For instance, a Three-level procedure parameter means four degrees of flexibility. The degrees of opportunity connected with collaboration between two procedure parameters are given by the result of the degrees of flexibility for the two procedure parameters.
5.2
Levels Of Control Factors-
Fig 5.1 Minitab window [5]
33
Table 5.1 Levels of control component
Level
Feed (mm/rev)
Depth (mm)
Speed (RPM)
1
0.1
0.1
2000
2
0.15
0.2
2500
3
0.20
0.25
3000
Table 5.2 Levels of control elements
Run
Columns
1
2
3
1
1
1
1
2
1
2
2
3
1
3
3
4
2
1
3
5
2
2
1
6
2
3
2
7
3
1
2
8
3
2
3
9
3
3
1
Fig 5.2 Turning Procedure [6]
34
Fig 5.3 CNC CUB/XXZ servo controlled turret lathe Table 5.3 L9 orthogonal exhibit framework
Cutting speed
Run
Feed (mm/rev)
Depth (mm)
Speed (RPM)
1
0.1
0.1
2000
188.32
2
0.1
0.2
2500
235.40
3
0.1
0.25
3000
282.48
4
0.15
0.1
3000
282.48
5
0.15
0.2
2000
188.32
6
0.15
0.25
2500
235.40
7
0.2
0.1
2500
235.40
8
0.2
0.2
3000
282.48
9
0.2
0.25
2000
188.32
35
(m/min)
Table 5.4 Result framework for surface roughness
Cutting
Feed
Depth
Speed
(mm/rev)
(mm)
(RPM)
1
0.1
0.1
2000
188.32
2
0.1
0.2
2500
3
0.1
0.25
4
0.15
5
Run
speed
R a
SNRA1
MEAN1
0.409
-7.7655
0.409
235.40
0.997
-0.0261
0.997
3000
282.48
0.959
-0.3636
0.959
0.1
3000
282.48
1.700
4.6090
1.700
0.15
0.2
2000
188.32
1.688
4.5474
1.688
6
0.15
0.25
2500
235.40
3.425
10.6932
3.425
7
0.2
0.1
2500
235.40
3.422
10.6856
3.422
8
0.2
0.2
3000
282.48
3.002
9.5482
3.002
9
0.2
0.25
2000
188.32
1.736
4.7910
1.736
(m/min)
(µm)
Table 5.5 Result framework for Material Removal Rate
Cutting
MRR
speed
(cc/min)
SNRA2
MEAN2
188.32
1.8832
5.4979
1.8832
2500
235.40
4.708
13.4567
4.7080
0.25
3000
282.48
7.062
16.9786
7.0620
0.15
0.1
3000
282.48
4.2372
12.5416
4.2372
5
0.15
0.2
2000
188.32
5.6496
15.0404
5.6496
6
0.15
0.25
2500
235.40
8.8275
18.9168
8.8275
7
0.2
0.1
2500
235.40
4.708
13.4567
4.7080
8
0.2
0.2
3000
282.48
11.2992
21.0610
11.2992
9
0.2
0.25
2000
188.32
9.416
19.4773
9.4160
Feed
Depth
Speed
(mm/rev)
(mm)
(RPM)
1
0.1
0.1
2000
2
0.1
0.2
3
0.1
4
Run
36
(m/min)
5.3
Analysis-
At first the analysis is done for surface roughness and thereafter moving towards material removal rate. When you add a signal factor to an existing static design, Minitab adds a new signal factor column after the factor columns and appends new rows (replicates) to the end of the existing worksheet. For example, if you add a signal factor with 2 levels to 3
an existing L4 (2 ) array, 4 rows (1 replicate of 4 runs) are added to the worksheet. If you add a signal factor with 3 levels, 8 rows (2 replicates of 4 runs) are added to the worksheet. A replicate is the entire set of runs from the static design. In a usual Taguchi robust parameter design experiment, you would subject each control factor combination to each of the noise conditions and measure the response variable. If you are doing a dynamic experiment, the response is measured at each level of the signal factor. Record the results for each noise condition in a separate response column in the worksheet.
Main Effects Pl ot for Means
Data Means Me ans A
3.0
B
2.5 2.0 1.5
s n a 1.0 e M f o n a e 3.0 M
1
2
3
C
2.5 2.0 1.5 1.0 1
2
3
Fig 5.4 Plot for means
37
1
2
3
Use the response tables to select the best level for each factor. Usually you have the following objectives with a Taguchi design:
Minimize the standard deviation
Maximize the S/N ratio
Meet a target with the mean (static design)
Meet a target with the slope (dynamic design)
Use the delta and rank values to identify the factors that have the largest effect on each response characteristic. Then, determine which levels of these factors meet your objectives. Sometimes, the best level of a factor for one response characteristic is different from the best level for a different response characteristic. To resolve this issue, it may help to predict the results for several combinations of factors levels to see which one produces the best result.
5.3.1 Interpretation
Average response characteristics- For each factor, Minitab calculate the average
of the response characteristic at each level of the factor. For example, the design includes factor A at 2 levels (1 and 2) and 4 measurements at each level. Minitab calculates the mean of the 4 S/N ratios at level 1 and the mean of the other 4 S/N ratios at level 2.
Signal-to-Noise Ratio- Minitab calculates a separate signal-to-noise ratio (S/N)
for each combination of control factor levels in the design. You can choose from different S/N ratios, depending on the goal of your experiment. In all cases, you want to maximize the S/N ratio.
Means (for static designs)- Minitab calculates a separate mean for each
combination of control factor levels in the design.
Slopes (for dynamic designs)- Minitab calculates a separate slope for each
combination of control factor levels in the design.
Standard deviations- Minitab calculates a separate standard deviation for each
combination of control factor levels in the design.
38
Delta- Measures the size of the effect by taking the difference between the
highest and lowest characteristic average for a factor.
Rank- The ranks in a response table help you quickly identify which factors have
the largest effect. The factor with the largest delta value is given rank 1, the factor with the second largest delta is given rank 2, and so on. In these results, the response tables show the following:
For the Signal to Noise Ratios, A is ranked 1, follo wed by B and C.
For the Means, A is ranked 1, followed b y C and B
Here feed, depth and speed are our ou r factor A, B and C respectively. Table 5.6 Response table for m eans
Level
Feed
Depth
Speed
1
0.7883
1.8437
2.2787
2
2.2710
1.8957
1.4777
3
2.7200
2.0400
2.0230
Delta
1.9317
0.1963
0.8010
Rank
1
3
2
We want to maximize the S/N ratio and the mean. For example, for factor B, the average S/N ratio for all runs with level 1 is 2.510, the average for runs with level 2 is 4.690 and the average for runs with level 3 is 5.040. This indicates that level 3 maximizes the signal-to-noise ratio. Here from the response table for the means we can conclude that the level 3, level 3 and level 1 maximizes the mean values for factors A, B and C respectively. The above combination of level is also the part of our L9 orthogonal array generated using Minitab, run 9. This will help us further in concluding about the requirement of confirmation test for our experiment. A similar type of conclusion can also be made for signal-to-noise ratio further.
39
Main Effects Plot for SN ratios
Data Means A
10
B
5 s o i t 0 a r N S f o n 10 a e M
1
2
3
1
2
3
C
5 0 1
2
3
Signal-to-noise: Larger is better Fig 5.5 Plot for S/N proportion Table 5.7 Response Table for signal to noise ratio
Level
Feed
Depth
Speed
1
-2.718
2.510
4.956
2
6.617
4.690
3.125
3
8.342
5.040
4.159
Delta
11.060
2.531
1.832
Rank
1
2
3
Here from the response we have concluded that the level 3, level 3 and level 1 maximizes our signal-to-noise ratio for our factors A, B & C respectively, namely Feed (mm/rev), depth of cut (mm) and spindle speed (RPM) respectively. This combination of levels maximizing the signal-to-noise ratio for our experiment is also the part of our L9 orthogonal array, run 9.
40
Now moving towards our material removal rate. From result framework for material removal rate we plot two graphs for means and sign al to noise ratios respectively.
Main Effects Plot for Means
Data Means A
B
8 7 6 s 5 n a e 4 M f o n a e M8
1
2 C
3
1
2
3
1
2
3
7 6 5 4
Fig 5.6 Plot for Means for MRR Table 5.8 Response table for m eans
Level
Feed
Depth
Speed
1
4.551
3.609
7.337
2
6.238
7.219
6.120
3
8.474
8.435
5.807
Delta
3.923
4.826
1.530
Rank
2
1
3
Here from the response table for the means we can conclude that the level 3, level 3 and level 1 maximizes the mean values for factors A, B and C respectively. The above combination of level is also the part of our L9 orthogonal array generated using Minitab,
41
run 9. This will help us further in concluding about the requirement of confirmation test for our experiment on calculation of Material Removal Rate.
Main Effects Effects Plot for S N ratios
Data Means A
B
18 16 14
s o i t 12 a r N10 S f o n a e 18 M
1
2 C
3
1
2
3
1
2
3
16 14 12 10
Signal-to-noise: Larger is better
Fig 5.7 Plot for S/N ratio for MRR Table 5.9 Response table for signal-to-noise ratio
Level
Feed
Depth
Speed
1
11.98
10.50
15.16
2
15.50
16.52
15.16
3
18.00
18.46
15.16
Delta
6.02
7.96
0.00
Rank
2
1
3
Here from the response table for the means we can conclude that the level 3, level 3 and level 1 maximizes the mean values for factors A, B and C respectively. The above combination of level is also the part of our L9 orthogonal array generated using Minitab, run 9.
42
5.3.2 Conformation Experiments We can conclude that for our surface roughness the required combination to maximize the means and signal-to-noise ratios is of the order 3, 3, and 1is the part of our L9 orthogonal exhibit framework. Therefore we do not require any confirmation test for our surface roughness measurement. We can conclude that for our Material Required Rate, the required combination to maximize the means and signal-to-noise ratios is of the order 3, 3, and 1is the part of our L9 orthogonal exhibit framework. Therefore we do not require any confirmation test for our Material Removal Rate. Table 5.10 Optimum table for Surface roughness
Exp. No.
Optimum Combination
Feed (mm/rev)
1
A3B3C1
0.2
Depth (mm)
0.25
Speed (RPM)
2000
Table 5.11 Optimum table for Material Removal Rate
Exp. No.
Optimum Combination
Feed (mm/rev)
1
A3B3C1
0.2
43
Depth (mm)
0.25
Speed (RPM)
2000
Fig 5.8 Turned workpiece(s)
44
CHAPTER 6 RESULTS AND CONCLUSION In the investigation, the aggregate of squares and fluctuation are ascertained. F-test quality at 95% certainty level is utilized to choose the critical elements influencing the procedure and rate commitment is as certained.
DF- The total degrees of freedom (DF) are the amount of information in your
data. The analysis uses that information to estimate the values of unknown population parameters. The total DF is determined by b y the number numbe r of observations o bservations in your experiment. The DF for a term show how much information that term uses. Increasing your sample size provides more information about the population, which increases the total DF. Increasing the number of terms in your model uses more information, which decreases the DF available to estimate the variability of the parameter estimates.
Seq SS- Sequential sums of squares are measures of variation for different
components of the model. Unlike the adjusted sums of squares, the sequential sums of squares depend on the order the terms are entered into the model. In the Analysis of Variance table, Minitab lists the sequential sums of squares for the main effects, interactions, and error term.
Adj SS- Adjusted sums of squares are measures of variation for different
components of the model. The order of the predictors in the model does not affect the calculation of the adjusted sum of squares. In the Analysis of Variance table, Minitab separates the sums of squares into different components that describe the variation due to different sources.
Adj MS- Adjusted mean squares measure how much variation a term or a model
explains, assuming that all other terms are in the model, regardless of the order they were entered. Unlike the adjusted sums of squares, the adjusted mean squares consider the degrees of freedom. The adjusted mean square of the error (also 2
called MSE or s ) is the variance around the fitted values.
45
F-value- The Analysis of Variance table lists an F-value for each term. The F-
value is the test statistic used to determine whether the term is associated with the response.
P-Value- The p-value is a probability that measures the evidence against the null
hypothesis. Lower probabilities provide stronger evidence against the null hypothesis. The ANOVA investigation for rate adjustment is demonstrated in the table Table 6.1 Analysis of Variance for Ra (µm), using Adjusted SS for Tests
Source
DF
Seq SS
Adj SS
Adj MS
F
P
Feed
2
6.131
6.131
3.066
2.28
0.305
Depth
2
0.062
0.062
0.031
0.02
0.977
Speed
2
1.004
1.004
0.502
0.37
0.728
Error
2
2.688
2.688
1.344
Total
8
9.886
S = 1.15939 R-Sq = 72.81% R-Sq(adj) = 0.00% Table 6.2 Analysis of Variance for MRR (cc/min), using Adjusted SS for Tests
Source
DF
Seq SS
Adj SS
Adj Adj MS
F
P
Feed
2
23.240
23.240
11.620
3.98
0.201
Depth
2
37.795
37.795
18.897
6.47
0.134
Speed
2
3.919
3.919
1.959
0.67
0.598
Error
2
5.840
5.840
2.920
Total
8
70.793
S = 1.70879 R-Sq = 91.75% R-Sq(adj) = 67.00% From the experiment we can conclude that with increased feed and reduced spindle speed we can get a better surface finish. Meanwhile with increased depth of cut and reduced spindle speed, we can get a better material removal rate. Percentage combination of which can be observed from the Analysis of Variance done above.
46
REFERENCES [1] Prajapati, Navneet K. and Patel, S. M., “Optimization of process parameters for surface roughness and material removal rate for SS 316 on CNC turning machine”. “International Journal of Research in Modern Engineering an d Emerging Technology”, Technology”, Vol. 1, Issue: 3, pp.4047, 2013. 2013. [2] Zhang, Julie Z., Chen, Joseph C. and Kirby, E. Daniel, “Surface roughness optimization in an end-milling operation using the Taguchi design method”. “ Journal of Materials Processing Technology”, Technology”, Vol.184, pp. 233–239, 2007. [3] Joshi, A., Kothiyal, P., “Investigating effect of machining parameters of CNC milling on surface finish by Taguchi method”, method”, “ International Journal on Theoretical and Applied Research in Mechanical Engineering”, Engineering”, Volume-2, Issue-2, pp. 113-119, 2013. [4] Reddy, B. Sidda, Kumar, J. Suresh and Reddy K. Vijaya Kumar, “Optimization of surface roughness in CNC end milling using response surface methodology and genetic algorithm.”, “International Journal of Engineering, Science and Technology”, Technology”, Vol. 3, No. 8, pp. 102-109, 2011. [5] Kromanis, A., Krizbergs, J., “3d Surface roughness prediction technique in end milling using regression analysis.”, “6th International DAAAM Baltic Conference Industrial Engineering”, Engineering”, 2008. [6] Bajic, D., Lele, B., Zivkovic, D., “Modeling of machined surface roughness and optimization of cutting parameters in face milling.”, “Journal Of Reinforced Plastics”, Plastics”, Vol.47, pp.331-334, 2008. [7] Chockalingam, P., Wee Lee Hong, “Surface Roughness and Tool Wear Study on Milling of AISI 304 Stainless Steel Using Different Cooling Conditions.”, “International Journal of Engineering and Technology”, Technology”, Vol. 2, No. 8, pp.1386-1392, 2012. [8] Rajput, R., Sarathe, K., A., “Comparative Study of CNC Controllers used in CNC Milling Machine”, “American Journal of Engineering Research (AJER)”, (AJER)”, e-ISSN: 2320-0847, p-ISSN : 2320-0936, Volume-5, Issue-4, pp-54-62, 2010. 47
[9] Sureshkannan, G., Dhandapani, N., V., Thangarasu, V., S., “Investigation on Effect of Material Hardness in High Speed CNC End Milling Process”, “Hindawi Publishing Corporation The Scientific World Journal”, Journal”, Volume 2015, Article ID 762604, 6 pages, pa ges, 2015.
[10] Makwana, R., D., Viramgama, K., M., “A STUDY ON DESIGN OF FIXTURE FOR VALVE BODY FOR CNC MACHINE”, “International Journal of Advance Engineering and Research Development”, Development”, Volume 1, Issue 12, 2012. rd
[11] Roy, R.K.; “Design of Experiments Experiments using Taguchi Approach” Approach” Edition 3 Page No. 34-39
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